basic need Vector Vector Vector

GitHub Repo https://github.com/UmerBaig123/CPPFunctions

UmerBaig123/CPPFunctions

Thanks to C++ not having many built in functions for vectors and string, I have made header files to do basic tasks we need to do on string or vectors

GitHub Repo https://github.com/porglezomp/graphicsmath

porglezomp/graphicsmath

A small library to handle basic vector/matrix math needed for computer graphics, with readable syntax in the style of GLSL.

GitHub Repo https://github.com/EwSwi/crispy_helper

EwSwi/crispy_helper

easy helper for vectors calc needed for crispy front end (for quanty), executed with python, basic linear algebra

GitHub Repo https://github.com/AsTeriaa09/Langchain-ollama-chatbot

AsTeriaa09/Langchain-ollama-chatbot

All the basic to required advanced concepts you need to know for creating an Ollama application using langchain and vector.

GitHub Repo https://github.com/degr8noble/Support-Vector-Machine-_with_python

degr8noble/Support-Vector-Machine-_with_python

In this notebook, we introduce the Support Vector Machine (SVM) algorithm, a powerful, but simple supervised learning approach to predicting data. For classification tasks, the SVM algorithm attempts to divide data in the feature space into distinct categories. By default, this division is performed by constructing hyperplanes that optimally divide the data. For regression, the hyperplanes are constructed to map the distribution of data. In both cases, these hyperplanes map linear structures in a non-probabilistic manner. By employing a _kernel trick_, however, we can transform non-linear data sets into linear ones, thus enabling SVM to be applied to non-linear problems. SVMs are powerful algorithms that have gained widespread popularity. This is partly due to the fact that they are effective in high dimensional feature spaces, including those problems where the number of features is similar to or slightly exceeds the number of instances. Unlike KNN, which has high demand on memory with large dataset, SVMs can be memory efficient since only the support vectors are needed to compute the hyperplanes. Finally, by using different kernels, SVM can be applied to a wide range of learning tasks. On the other hand, these models are black boxes, and it can be difficult to explain how they operate, especially on new instances. They do not, by default, provide probability estimates since the hyperplane is constructed to cleanly divide the training data. In this notebook, we first explore the basic formalism of the SVM algorithm, including the construction of hyperplane and the kernel trick, which enables SVM to be applied to non-linear problems. Next, we explore the application of SVM to classification problems, which is known as support vector classification, or SVC. To introduce this topic, we will once again use the Iris data to construct an SVC estimator, plot the calculated hyperplane, explore the resulting performance. Next, we will switch to a more complex data set, the adult data. Finally, we will apply SVM to regression problems, which is known as support vector regression. For this we will use the MPG data introduced in previous lessons.

GitHub Repo https://github.com/UselessBen1/Static_vector_calculation

UselessBen1/Static_vector_calculation

calculate basic need in vector with cooridnates. IE unit vector, dot product.

GitHub Repo https://github.com/unais5/Vector-Space-Model

unais5/Vector-Space-Model

The query processing of VSM is quite tricky, you need of optimize every aspect of computation. The high-dimensional vector product and similarity values of query (q) and documents (d) need to optimized. Basic Assumption for Vector Space Model (VSM) Retrieval Model 1.Simple model based on linear algebra. Terms are considered as features using a weighting scheme. 2.Allows partial matching of documents with the queries. Hence, able to produce good institutive scoring. Continuous scoring between queries and documents. 3.Ranking of documents are possible using relevance score between document and query.

GitHub Repo https://github.com/banana-galaxy/trigonometry

banana-galaxy/trigonometry

shows basic trigonometry concepts/functions at work need to make games involving vector coordinates

GitHub Repo https://github.com/saaimzr/Encoder-Decoder-Transformer-Model-for-Vector-to-Vector-Computation

saaimzr/Encoder-Decoder-Transformer-Model-for-Vector-to-Vector-Computation

A Transformer model built from scratch to perform basic arithmetic operations, implementing multi-head attention, feed-forward layers, and layer normalization from the Attention is All You Need paper. Trained on a toy dataset for addition and subtraction, it visualizes attention weights to show learning patterns in sequence-to-sequence tasks.

GitHub Repo https://github.com/pedjamitrovic/vectorpaint

pedjamitrovic/vectorpaint

Graphics editor where each graphical figure is represented as an object. There are few supported basic figures which you can draw, and do basic manipulation with them such as changing color, line width, figure size in pixels, x/y coordinates on canvas etc. Picture can be saved as file in internal format and opened later if needed. Implemented in Java. Implemented in Java.